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   "source": [
    "**酶活性预测实战task：**  \n",
    "1、基于T-R-train.csv数据，建立线性回归模型，计算其在T-R-test.csv数据上的r2分数，可视化模型预测结果  \n",
    "2、加入多项式特征（2次、5次），建立回归模型  \n",
    "3、计算多项式回归模型对测试数据进行预测的r2分数，判断哪个模型预测更准确  \n",
    "4、可视化多项式回归模型数据预测结果，判断哪个模型预测更准确  "
   ],
   "cell_type": "markdown",
   "metadata": {}
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   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "output_type": "execute_result",
     "data": {
      "text/plain": "       T  rate\n0  46.53  2.49\n1  48.14  2.56\n2  50.15  2.63\n3  51.36  2.69\n4  52.57  2.74",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>T</th>\n      <th>rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>46.53</td>\n      <td>2.49</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>48.14</td>\n      <td>2.56</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>50.15</td>\n      <td>2.63</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>51.36</td>\n      <td>2.69</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>52.57</td>\n      <td>2.74</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# 加载数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data_train = pd.read_csv('T-R-train.csv')\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0     46.53\n1     48.14\n2     50.15\n3     51.36\n4     52.57\n5     54.18\n6     56.19\n7     58.58\n8     61.37\n9     63.34\n10    65.31\n11    66.47\n12    68.03\n13    69.97\n14    71.13\n15    71.89\n16    73.05\n17    74.21\nName: T, dtype: float64 0     2.49\n1     2.56\n2     2.63\n3     2.69\n4     2.74\n5     2.80\n6     2.88\n7     2.92\n8     2.96\n9     2.95\n10    2.91\n11    2.85\n12    2.78\n13    2.69\n14    2.61\n15    2.54\n16    2.45\n17    2.39\nName: rate, dtype: float64\n"
    }
   ],
   "source": [
    "# X，y复制\n",
    "X_train = data_train.loc[:, 'T']\n",
    "y_train = data_train.loc[:, 'rate']\n",
    "print(X_train, y_train)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化\n",
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "plt.scatter(X_train, y_train)\n",
    "plt.title('raw data')\n",
    "plt.xlabel('temperature')\n",
    "plt.ylabel('rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LinearRegression()"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# 线性回归模型\n",
    "X_train = np.array(X_train).reshape(-1, 1)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr1 = LinearRegression()\n",
    "lr1.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 加载测试数据\n",
    "data_test = pd.read_csv('T-R-test.csv')\n",
    "data_test.head()\n",
    "X_test = data_test.loc[:, 'T']\n",
    "y_test = data_test.loc[:, 'rate']\n",
    "X_test = np.array(X_test).reshape(-1, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.016665703886981964 -0.7583363437351314\n"
    }
   ],
   "source": [
    "# 预测并测试\n",
    "y_train_predict = lr1.predict(X_train)\n",
    "y_test_predict = lr1.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r2_train = r2_score(y_train, y_train_predict)\n",
    "r2_test = r2_score(y_test, y_test_predict)\n",
    "print(r2_train, r2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 创建新数据\n",
    "X_range = np.linspace(40, 90, 300).reshape(-1, 1)\n",
    "y_range_predict = lr1.predict(X_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化\n",
    "fig1 = plt.figure(figsize=(5, 5))\n",
    "plt.plot(X_range, y_range_predict)\n",
    "plt.scatter(X_train, y_train)\n",
    "plt.title('prediction data')\n",
    "plt.xlabel('temperature')\n",
    "plt.ylabel('rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[1.0000000e+00 4.6530000e+01 2.1650409e+03]\n [1.0000000e+00 4.8140000e+01 2.3174596e+03]\n [1.0000000e+00 5.0150000e+01 2.5150225e+03]\n [1.0000000e+00 5.1360000e+01 2.6378496e+03]\n [1.0000000e+00 5.2570000e+01 2.7636049e+03]\n [1.0000000e+00 5.4180000e+01 2.9354724e+03]\n [1.0000000e+00 5.6190000e+01 3.1573161e+03]\n [1.0000000e+00 5.8580000e+01 3.4316164e+03]\n [1.0000000e+00 6.1370000e+01 3.7662769e+03]\n [1.0000000e+00 6.3340000e+01 4.0119556e+03]\n [1.0000000e+00 6.5310000e+01 4.2653961e+03]\n [1.0000000e+00 6.6470000e+01 4.4182609e+03]\n [1.0000000e+00 6.8030000e+01 4.6280809e+03]\n [1.0000000e+00 6.9970000e+01 4.8958009e+03]\n [1.0000000e+00 7.1130000e+01 5.0594769e+03]\n [1.0000000e+00 7.1890000e+01 5.1681721e+03]\n [1.0000000e+00 7.3050000e+01 5.3363025e+03]\n [1.0000000e+00 7.4210000e+01 5.5071241e+03]]\n"
    }
   ],
   "source": [
    "# 多项式模型\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly2 = PolynomialFeatures(degree=2)\n",
    "X_2_train = poly2.fit_transform(X_train)\n",
    "X_2_test = poly2.transform(X_test)\n",
    "print(X_2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.970051540068943 0.996395455646867\n"
    }
   ],
   "source": [
    "lr2 = LinearRegression()\n",
    "lr2.fit(X_2_train, y_train)\n",
    "y_2_train_predict = lr2.predict(X_2_train)\n",
    "y_2_test_predict = lr2.predict(X_2_test)\n",
    "r2_2_train = r2_score(y_train, y_2_train_predict)\n",
    "r2_2_test = r2_score(y_test, y_2_test_predict)\n",
    "print(r2_2_train, r2_2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2_range = np.linspace(40, 90, 300).reshape(-1, 1)\n",
    "X_2_range = poly2.transform(X_2_range)\n",
    "y_2_range_predict = lr2.predict(X_2_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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5gbvezKXi4UidYY0ezjnuG7+IF6av495RvbhhWKegS5KjoPEoY1h5uePe8YuofM5Gdw6MHmbGPaN6s7lwH2PeWczGb4qYuHCLtvDjiI5RRrm/vL/ssNurHqAzrNEjMcF45KoBtM1M5enP1ui+4XFGQRnFXp+1gb9/vIrGVVxbrDOs0aVxchIlZYcfytJgJLFPQRmlvli5nbvfXMBp3Vrwh4v76Mx2jNi68/D7EIG2/mOdjlFGobXb93DLS7Pp1KIJj109iKYpIRITTGe2Y0CbzFQNRhKHFJQBGzd34yEBOPqsrjzz+RoSEoxnv38iTVPCt1HVILuxoaqBf7X1H9sUlAEaN3fjIf+oNhYUcfdbCynH8eIPhtCuWeOAK5QjVbl/ZWookaKSMtJT9E8tlum3F6AHJx9+RrvMOZqmJHFat6yAqpJjVXHrf19JGec+9Ak3vjALR/jSRx02iT06mROgqg7w79xXWs+ViF8mLdzCtl37Dw6Dp+5CsUlBGaCqDvBXNeCCxJ4HJy9jv+4THvN8C0oza2dmH5nZEjNbZGa3R1jmDDMrNLN53nSPX/VEo18O70FK0qG/AnX7iS9V7TWou1Bs8XOLshT4uXOuJ3Ay8GMz6xVhuWnOuQHeNMbHeqLOJQNzGNa1xcHnurd2/Klqr6FFWqN6rkSOhW8nc5xzm4HN3uNdZrYEyAEW+/WZ0ahy95+KB/I/XraNqUu38d0h7Q/eukHiS6TuQgAOx67Zr5P+6RgozIOMtnD2PdDvyoAqlerUyzFKM+sIDAS+jDB7qJnNN7OJZta7PuqpLwe6/0S67nfrzn38/PX59GiVzr2jIm1oSzyoeJ9wI7zXMPqsrnyzZz93v7UQV7ABcFC4ASaMhtzXgy5ZIvC9e5CZpQH/Bu5wzu2sNHsO0ME5t9vMRgLjgG4R1nEzcDNA+/btfa647kTq/lNUUsYDk5bSoXkT9haX8djVA0kJ6T7R8SzSxQKNvnqUB3efz1DO4ntJH4ZfLCmCqWO0VRmFfN2iNLMQ4ZB82Tn3ZuX5zrmdzrnd3uP3gJCZtYiw3JPOucHOucFZWbHTv7DKA/mF+5i+egdjLu5N15bp9VyVRINbS17itIRcxpRey6ry7P/MKMwLriipkp9nvQ14BljinHuoimVae8thZkO8enb4VVN9q+763lH923D5CW3rsRqJJgmZOfwl9AQpFHNnyW2UOG+vIkN/E9HIzy3KYcC1wFkVuv+MNLNbzOwWb5nLgYVmNh8YC1zlYm3I9WpEuq+KARmpIf54cR+8/yOkAZrZ5ac0ZS9/Cj3DfNeFv5VeQpFLZmaXnwZdmkTg51nvzwjnQnXLPAo86lcNQat8P5vGyYnsKS7jse8NIqNxKODqJEh3LO7GCSU38l9Jr3NZwqc8VnYJc8q7smZxNz6/KOjqpDJd6+2zAwfyp63I59pnvuKGYR05tdthh2GlgdlUUMRGTmV88akHX/vM9Qd1RI9KuoSxHhTuLeGXb+TStWUavzr/+KDLkShQ1fHrqkazl2ApKOvBPeMXsn33fh6+coC6AgkQ+fh1UoKxt7iMT5fnB1SVVEW73j6bsngrb8/bxM/O6U7fthlBlyNRItL92O84pxsPTl7GDc/NpMw5DckWRRSUPiosKuE3by3g+Nbp3HZml6DLkShTuSP6uLkbKdhbQpnX8ePAlVwHlpXgaNfbR396dwk79hTz4OX9CSXqq5bqPTh5GcVlGpItGulfr08+W7Gd12Zt4KbTOmuXW2pFQ7JFLwWlD/bsL+WuN3Pp3KIJd5xz2KXrIhFVdSZcd3AMnoLSBwduLPXny/vpLLfUWqQz4QBXDNZljUFTUNaV3Nfh4T7Mu2cQL3yxmuu6lXBix2ZBVyUxpPKQbNkZKWSkhpi8aCullY5dSv3SWe+6kPs6TBhNafF+flPyB1pSwC82/Q5yizVklhyRymfCJy7YzK0vz+H5L9Zy42mdA6ysYdMWZV2YOgZKinix7DwWuU7cG3qR9NJvwq+LHIPz+7TmrONb8tCU5TqpEyAFZV0ozGOLO46HSi/nWwnzGJHw1cHXRY6FmfH7i3pT7hz3jV8UdDkNloKyLmS05Q8l11JCEmOSnufg6GkaW1DqQLtmjbn97O68v3grUxZvDbqcBklBWQc+7vl73i0/mZ8mvUWHhG3hF0Op4ZtFidSBG0/rRPdWadz79kL27C8NupwGR0F5jPaVlHFPbjO6NC3npmbzAYOMdjBqrE7kSJ0JJSbwp0v7sqlwH2M/XBF0OQ2Oznofo//7ZDXrv97LKzcNpVGX+UGXI3FscMdmfHtQW579bA1XndieTi2aBF1Sg6EtymOwsaCIxz9ZyQX9sjmliwbjFf/9akQPGiUl8od3FgddSoOioDwGf3pvCQB3j+wZcCXSULRMT+H2s7vx4dJtfLhUJ3bqi4LyKM1YvYN3czdz67e6kqNrcaUeXX9KRzpnNWHMhMXsLy2r+Q1yzBSUR6G0rJz7xi8iJzOVH31LV0tI/UpOSuDeUb1Zu2Mvz362NuhyGgQF5VF4deYGlm7ZxW8u6KlBLyQQ3+qexbm9WvG3D1ewdee+oMuJewrKI1Swt5j/fX8ZQzs3Z0Sf1kGXIw3Y7y7oRWm54/6JS4MuJe4pKI/QIx+sYGdRCfde1Auzam9bLuKr9s0b88NTO/HW3I3k5hUEXU5cU1AegTXb9/DSjHVcNaQ9x7duGnQ5Itx2RheaN0nmv99dgvPutSN1T0F5BB6YtJTkpASNWi5RIz0lxB3ndufLNV/rOnAfKShrafa6r5m4cAs/Or0LLdNTgi5H5KDvntiOLllNuH/iUko0wK8vFJS14Jzjv99dQsv0Rtx0eqegyxE5RFJiAneP7Mnq7Xt45cv1QZcTlxSUtTBx4RbmrC/gznO70zhZl8dL9Dnr+Jac0qU5j3ywnJ37SoIuJ+4oKGtQXFrOnyctpXurNK4Y3C7ockQiMjPuHtmTgqISHvtoZdDlxB0FZQ1e/nId63bs5dcjepKYoO5AEr365GRw2cC2PPf5Wt02oo4pKKtRWFTC2KkrGNa1OWf0yAq6HJEa3Xled3AwdqrGrKxLCspqPPHJKgqKSvj1iJ7qXC4xISczlatPbs8bs/NYlb876HLihoKyCtt27eO5z9dwUf829MnJCLockVr78ZldaZSUwEPvLw+6lLihoKzC39+YRElJCT9bfBU83Cd8726RGNAirRE3ntqJdxdsZuHGwqDLiQsKygjypr/BK8vhisRP6JiwBQo3wITRCkuJGTee3pnMxiEemLws6FLigoIygrGTw/dPHp305n9eLCmCqWMCqkjkyDRNCXHbGV34dHk+M1bvCLqcmKegrGR1/m7+vW8QVyd+QBv7+tCZhXnBFCVyFK4b2pHWTVN4YNJSDZhxjBSUlTz8wQqSKeO2pLcPn5nRtv4LEjlKKaFERp/djTnrC5i6ZFvQ5cQ0BWUFSzbvZML8TdzQC7KSK10GFkqFs+8JpjCRo3TF4LZ0bN6Yv7y/jPJybVUeLQVlBf/7/nLSU5L40eUXwqixkNEOsPDPUWOh35VBlyhyREKJCfzs3O4s3bKLiQu3BF1OzPItKM2snZl9ZGZLzGyRmd0eYRkzs7FmttLMcs1skF/11GTu+m/4YMlWbj6tMxmNQ+FQ/NlCuK8g/FMhKTHqwn5t6JLVhL9OXa6tyqPk5xZlKfBz51xP4GTgx2bWq9IyI4Bu3nQz8LiP9VTroSnLadYkmRtO1TBqEl8SE4zRZ3dj+dbdTFqkrcqj4VtQOuc2O+fmeI93AUuAnEqLXQy86MJmAJlmlu1XTVWZs/4bpq3Yzo9O70xaIw2jJvHn4FblByu0VXkU6uUYpZl1BAYCX1aalQNsqPA8j8PD1Hd/m7qC4xqHuObkDvX90SL14sBW5bKtu5j05+/BfZm64uwI+B6UZpYG/Bu4wzm3s/LsCG857L87M7vZzGaZ2az8/Pw6rS83r4CPluVz42mdaaKtSYljF/I5nRM2M3bn6ZQ7dMXZEfA1KM0sRDgkX3bOvRlhkTyg4mi4bYFNlRdyzj3pnBvsnBuclVW3w52NnbqSjNQQ1w3V1qTEt8SPxnB74r9Z6tozuXxw+EVdcVYrfp71NuAZYIlz7qEqFhsPXOed/T4ZKHTObfarpsoWbSrkgyVb+cGwTqSnhOrrY0WCUZjHhQnT6Wyb+Gvptyl3dvB1qZ6fW5TDgGuBs8xsnjeNNLNbzOwWb5n3gNXASuAp4DYf6znMox+uJL1REt8f1rE+P1YkGBltSTTH6KS3Dt2q1BVnNfLtoJxz7jMiH4OsuIwDfuxXDdVZ5nXAHX1WVzJStTUpDcDZ98CE0YxyXzDWLuWvpZcxvNEiEnTFWY0a7JU5f/twBU2SE/mB+k1KQ9HvShg1lsTMtvw0aRxLXQc+GPSoLqaohQYZlCu37eLdBZu5/pSOZDZODrockfrjXXE26r4JtGuWyt/XtNbIQrXQIIPy0Q9XkhpK5MbTOgddikggkhIT+NHpXZi3oYDpGq+yRg0uKDd8vZcJuZv53pD2NGuirUlpuC4/oS1Z6Y14/ONVQZcS9RpcUD41bTUJhrYmpcFLCSXyw1M7MW3Fdhbk6d461WlQQbl9935em7mBywa2pXVGStDliATu6pPa0zQlib9/vDLoUqJagwrKF75YS3FZOTd/S1uTIgDpKSGuG9qRSYu2sHKb7gNelQYTlLv3l/LCF2sZ3qs1XbLSgi5HJGrcMKwjjZISeOITHausSoMJyn9+tZ6d+0q55YwuQZciElWapzXiqhPbM27uRjYWFAVdTlRqEEFZXFrO09PWMLRzcwa0ywy6HJGoc9Pp4cNRT326OuBKolODCMpx8zayZec+bU2KVCEnM5WLB+Twz5nr2bF7f9DlRJ24D8rycscTn6yiV3ZTTu/WIuhyRKLWrWd0Zl9JOf+YsS7oUqJO3AfllCVbWZ2/h1vP6EJ45DcRiaRry3TO7JHFP6avY19JWdDlRJW4DkrnHI9/vIr2zRozok/roMsRiXo3ndaZHXuKeWvuxqBLiSpxHZSl5Y7Tu2dx+9ndSEqM66aK1ImhXZrTu01Tnp62Wjchq6BW6WFmjc3sd2b2lPe8m5ld6G9pxy6UmMCd53bn2ydoYFKR2jAzbjqtM6vy9/Dx8m1BlxM1aruZ9RywHxjqPc8D/uhLRSISqAv6ZZOdkcJTn64JupSoUdug7OKcewAoAXDOFVHD6OUiEptCiQl8/5SOTF+9g4UbNVgG1D4oi80sFe9WsmbWhfAWpojEoauGtKdJciJPT1MHdKh9UN4HTALamdnLwFTgV34VJSLBykgN8Z0T2zMhdzObdFlj7YLSOfc+cBnwfeBVYLBz7iMf6xKRgN0wrCPOOZ7/Ym3QpQSutme9pzrndjjn3nXOveOc225mU/0uTkSC065ZY0b0zebVL9eza19J0OUEqtqgNLMUM2sGtDCz48ysmTd1BNrUR4EiEpybTuvMrv2lvDErL+hSAlXTFuWPgNnA8d7PA9PbwGP+liYiQRvQLpOB7TN5cfraBt0BvdqgdM791TnXCfiFc66zc66TN/V3zj1aTzWKSIC+f0pH1u7YyyfL84MuJTC1PZnzNzPrY2ZXmtl1Bya/ixOR4I3ok03L9EYN+qRObU/m3Av8zZvOBB4ALvKxLhGJEslJCVxzcgc+WZ7PqvyGeV+d2vajvBw4G9jinLsB6A808q0qEYkq3x3SnuTEBF5soFuVtQ3Kfc65cqDUzJoC2wDdylCkgchKb8SF/bL51+y8BtlVqMagtPBot7lmlgk8Rfis9xzgK59rE5Eocv0pHdlTXMa/Zje8rkI1BqVzzgEDnHMFzrkngHOB671dcBFpIPq3y2RQ+0xe+KLhdRWq7a73DDM7EcA5t9Y5l+tjTSISpb4/rFOD7CpU26A8E5huZqvMLNfMFpiZwlKkgRnRpzUt0xvxXAM7qZNUy+VG+FqFiMSEUGK4q9BDU5azKn83XbLSgi6pXtS2w/m6SJPfxYlI9PnukPaEEo1/TG84EaA7bonIEclKb8SIPtn8e04ee4tLgy6nXigoReSIXXNyB3btK+Wd+ZuDLqVeKChF5Iid2PE4urdK46UvG8but4JSRHhqmYgAABDhSURBVI6YmXH1SR3IzSskN68g6HJ8p6AUkaNy6aAcUkOJvDQj/rcqFZQicuRyX6fp4wO5pHwK42etpnDW60FX5CvfgtLMnjWzbWa2sIr5Z5hZoZnN86Z7/KpFROpQ7uswYTQUbuDqxCnsI5l/Txgffj1O+blF+Txwfg3LTHPODfCmMT7WIiJ1ZeoYKAnfwrZPwjoG2ApeLj4d90H8/hP2LSidc58CX/u1fhEJSOGhowddk/QBq1wOM75JD6gg/wV9jHKomc03s4lm1jvgWkSkNjLaHvL0woQZZLCblxJGBVSQ/4IMyjlAB+dcf8K3mBhX1YJmdrOZzTKzWfn5DWvUEpGoc/Y9EEo9+DTFSrg89AWTi/uxbde+AAvzT2BB6Zzb6Zzb7T1+DwiZWYsqln3SOTfYOTc4KyurXusUkUr6XQmjxkJGO8Agox1XDz+VUmdxe//v2o4eVOfMrDWw1TnnzGwI4dDeEVQ9InIE+l0ZnjydgaGLZ/DazA3c+q0uJCRYcLX5wM/uQa8C04EeZpZnZj80s1vM7BZvkcuBhWY2HxgLXOWNpi4iMeiqIe1Y//Vepq+Ov+0d37YonXPfrWH+o8Cjfn2+iNSv4b1bk5Ea4p8zNzCsa8SjaDEr6LPeIhInUkKJXDowh8kLt/D1nuKgy6lTCkoRqTNXDWlHcVk5b83dGHQpdUpBKSJ15vjWTRnQLpPXZq4nnk45KChFpE5ddWI7lm/dzZz18TP8moJSROrUqP5taJKcyGsz1wddSp1RUIpInWrSKIlR/dswYf5mdu0rCbqcOqGgFJE6950T21FUUsaEOLmnjoJSROrcgHaZHN86PW52vxWUIlLnzIzvnNiO+XmFLN60M+hyjpmCUkR8cenAHJKTEnh91oagSzlmCkoR8UVm42TO69WKcfM2sr+0LOhyjomCUkR8c8XgdhTsLeHDJduCLuWYKChFxDendm1Bq6aN+Nfs2B6nUkEpIr5JTDAuG9SWj5fnx/To5wpKEfHV5Se0pazcMS6GB8pQUIqIr7pkpTGofSb/mp0XswNlKChFxHeXnxAeKGPBxsKgSzkqCkoR8d0F/bJplJQQsyd1FJQi4ruM1BDDe7fm7Xmb2FcSe30qFZQiUi+uGNyWwqISpsZgn0oFpYjUi1O6tCA7I4V/zY69SxoVlCJSL8J9KnP4ZHk+23bGVp9KBaWI1JtvD2pLuYM3Y6xPpYJSROpN56w0TuhwHG/Oia0+lQpKEalXlw7MYfnW3SzeHDvjVCooRaReXdA3m1Ci8dac2Nn9VlCKSL06rkkyZ/ZoydvzN1FWHhu73wpKEal3lw7MIX/Xfj5fuT3oUmpFQSki9e7M41vSNCUpZkYUUlCKSL1LCSVyQb9sJi3awt7i0qDLqZGCUkQCcenAtuwtLuP9RVuDLqVGCkoRCcTgDseRk5kaE53PFZQiEoiEBOOSgW34bEX03yZCQSkigbl0YA7lDsbP2xR0KdVSUIpIYLq2TKdvTgbj5kX37reCUkQCdenAHBZu3MmKrbuCLqVKCkoRCdSo/m1ITDDeiuKTOgpKEQlUVnojTu3agrfnbYraEYUUlCISuIv6t2FjQRFz1hcEXUpECkoRCdx5vVvRKCmBCfOj8+y3glJEApeeEuKs41vyTu5mSsvKgy7nML4FpZk9a2bbzGxhFfPNzMaa2UozyzWzQX7VIiLRb1T/NmzfvZ8v13wddCmH8XOL8nng/GrmjwC6edPNwOM+1iIiUe6s41uS1igpKjuf+xaUzrlPger+a7gYeNGFzQAyzSzbr3pEJLqlhBI5r1crJi7czP7SsqDLOUSQxyhzgIo3+M3zXhORBmpU/zbs3FfKtOXRNaBvkEFpEV6L2InKzG42s1lmNis/P9/nskQkKKd2a8FxjUOMj7Kz30EGZR7QrsLztkDEb8c596RzbrBzbnBWVla9FCci9S+UmMCIvtlMWbw1qgb0DTIoxwPXeWe/TwYKnXObA6xHRKLAqH5tKCop44Ml24Iu5SA/uwe9CkwHephZnpn90MxuMbNbvEXeA1YDK4GngNv8qkVEYseQTs1o1bRRVHU+T/Jrxc6579Yw3wE/9uvzRSQ2JSYYF/Rtw0sz1lFYVEJGaijoknRljohEn4sGtKG4rJzJC7cEXQqgoBSRKNS/bQbtmzVmQm507H4rKEUk6pgZo/pn8/nK7eTv2h90OQpKEYlOF/UP30/nvQXBd4ZRUIpIVOrROp0erdJ5Jwp2vxWUIhK1RvbNZta6b9i6M9jb2SooRSRqXdCvNc7BxIB3vxWUIhK1urZMp3urNN5bEGw3IQWliES1kX2zmbnua7YFuPutoBSRqHZB3+zw7neAnc8VlCIS1bq1SqdbyzTeDfA4pYJSRKLeyL7ZzFwb3O63glJEot4F/cK735MWBbP7raAUkajXvVU6XVumBXaVjoJSRGLCyL7ZfLXm60Cu/VZQikhMuKBvNuUB7X4rKEUkJnRvlUaXrCa8l1v/u98KShGJCWbGBX2z+XLNDrbvrt/dbwWliMSMkf283e967nyuoBSRmNGjVTqdWzSp97PfCkoRiRlmxsi+2cxYXb+73wpKEYkpI72z35Pr8ey3glJEYkrP7HQ6tWjCxHocek1BKSIxxcwY3rs1M1bvoHBvSb18poJSRGLO+X1aU1rumLp0a718noJSRGJOv5wMWjdNqbduQgpKEYk5CQnG8N6t+HRFPnuLS/3/PN8/QUTEB8N7t2ZfSTmfLs/3/bMUlCISk4Z0akZm4xCTF/l/nFJBKSIxKSkxgXN6tuKDJVspLi339bMUlCISs4b3bs2ufaXMWL3D189RUIpIzDqtWwsaJyf6PkalglJEYlZKKJEzemTx/qKtlJU73z5HQSkiMW1479Zs372fueu/8e0zFJQiEtPOPL4loUTzdZAMBaWIxLSmKSGGdW3BpEVbcM6f3W8FpYjEvOG9W7Ph6yKWbN7ly/oVlCIS887t1Qoz/+7QqKAUkZjXIq0RJ3ZoxvsKShGRqg3v05qlW3axdvueOl+3glJE4sJ5vVoB/ux++xqUZna+mS0zs5VmdleE+WeYWaGZzfOme/ysR0TiV7tmjendpilTFtf9IBm+BaWZJQKPASOAXsB3zaxXhEWnOecGeNMYv+oRkfh3bq9WzFn/Dfm76vYOjX5uUQ4BVjrnVjvnioF/Ahf7+Hki0sCd16s1zsHUJXW7VelnUOYAGyo8z/Neq2yomc03s4lm1tvHekQkzvXMTicnM7XOd7/9DEqL8FrlbvNzgA7Ouf7A34BxEVdkdrOZzTKzWfn5/o9mLCKxycw4r3crpq3czp79dXeLCD+DMg9oV+F5W2BTxQWcczudc7u9x+8BITNrUXlFzrknnXODnXODs7KyfCxZRGLdub1aUVxazrQV2+tsnX4G5Uygm5l1MrNk4CpgfMUFzKy1mZn3eIhXj78jcIpIXBvSsRlv3DKUc73uQnUhqc7WVIlzrtTMfgJMBhKBZ51zi8zsFm/+E8DlwK1mVgoUAVc5v65qF5EGISkxgRM7NqvTdVqs5dLgwYPdrFmzgi5DROKMmc12zg2ONE9X5oiI1EBBKSJSAwWliEgNFJQiIjVQUIqI1EBBKSJSAwWliEgNFJQiIjVQUIqI1CDmrswxs3xg3RG+rQVQd1fIByte2hIv7QC1JVodaVs6OOcijroTc0F5NMxsVlWXJsWaeGlLvLQD1JZoVZdt0a63iEgNFJQiIjVoKEH5ZNAF1KF4aUu8tAPUlmhVZ21pEMcoRUSORUPZohQROWpxGZRmlmhmc83sHe95MzObYmYrvJ/HBV1jbZjZWjNbYGbzzGyW91qstiXTzP5lZkvNbImZDY3FtphZD+/3cWDaaWZ3xGh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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(5, 5))\n",
    "plt.plot(X_range, y_2_range_predict)\n",
    "plt.scatter(X_train, y_train)\n",
    "plt.scatter(X_test, y_test)\n",
    "plt.title('prediction_2 data')\n",
    "plt.xlabel('temperature')\n",
    "plt.ylabel('rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[1.00000000e+00 4.65300000e+01 2.16504090e+03 1.00739353e+05\n  4.68740210e+06 2.18104820e+08]\n [1.00000000e+00 4.81400000e+01 2.31745960e+03 1.11562505e+05\n  5.37061900e+06 2.58541599e+08]\n [1.00000000e+00 5.01500000e+01 2.51502250e+03 1.26128378e+05\n  6.32533818e+06 3.17215710e+08]\n [1.00000000e+00 5.13600000e+01 2.63784960e+03 1.35479955e+05\n  6.95825051e+06 3.57375746e+08]\n [1.00000000e+00 5.25700000e+01 2.76360490e+03 1.45282710e+05\n  7.63751204e+06 4.01504008e+08]\n [1.00000000e+00 5.41800000e+01 2.93547240e+03 1.59043895e+05\n  8.61699821e+06 4.66868963e+08]\n [1.00000000e+00 5.61900000e+01 3.15731610e+03 1.77409592e+05\n  9.96864496e+06 5.60138160e+08]\n [1.00000000e+00 5.85800000e+01 3.43161640e+03 2.01024089e+05\n  1.17759911e+07 6.89837560e+08]\n [1.00000000e+00 6.13700000e+01 3.76627690e+03 2.31136413e+05\n  1.41848417e+07 8.70523734e+08]\n [1.00000000e+00 6.33400000e+01 4.01195560e+03 2.54117268e+05\n  1.60957877e+07 1.01950720e+09]\n [1.00000000e+00 6.53100000e+01 4.26539610e+03 2.78573019e+05\n  1.81936039e+07 1.18822427e+09]\n [1.00000000e+00 6.64700000e+01 4.41826090e+03 2.93681802e+05\n  1.95210294e+07 1.29756282e+09]\n [1.00000000e+00 6.80300000e+01 4.62808090e+03 3.14848344e+05\n  2.14191328e+07 1.45714361e+09]\n [1.00000000e+00 6.99700000e+01 4.89580090e+03 3.42559189e+05\n  2.39688665e+07 1.67710159e+09]\n [1.00000000e+00 7.11300000e+01 5.05947690e+03 3.59880592e+05\n  2.55983065e+07 1.82080754e+09]\n [1.00000000e+00 7.18900000e+01 5.16817210e+03 3.71539892e+05\n  2.67100029e+07 1.92018211e+09]\n [1.00000000e+00 7.30500000e+01 5.33630250e+03 3.89816898e+05\n  2.84761244e+07 2.08018089e+09]\n [1.00000000e+00 7.42100000e+01 5.50712410e+03 4.08683679e+05\n  3.03284159e+07 2.25067174e+09]]\n"
    }
   ],
   "source": [
    "poly5 = PolynomialFeatures(degree=5)\n",
    "X_5_train = poly5.fit_transform(X_train)\n",
    "X_5_test = poly5.transform(X_test)\n",
    "print(X_5_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.9978527267142803 0.5437845982167842\n"
    }
   ],
   "source": [
    "lr5 = LinearRegression()\n",
    "lr5.fit(X_5_train, y_train)\n",
    "y_5_train_predict = lr5.predict(X_5_train)\n",
    "y_5_test_predict = lr5.predict(X_5_test)\n",
    "r2_5_train = r2_score(y_train, y_5_train_predict)\n",
    "r2_5_test = r2_score(y_test, y_5_test_predict)\n",
    "print(r2_5_train, r2_5_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_5_range = np.linspace(40, 90, 300).reshape(-1, 1)\n",
    "X_5_range = poly5.transform(X_5_range)\n",
    "y_5_range_predict = lr5.predict(X_5_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(5, 5))\n",
    "plt.plot(X_range, y_5_range_predict)\n",
    "plt.scatter(X_train, y_train)\n",
    "plt.scatter(X_test, y_test)\n",
    "plt.title('prediction_5 data')\n",
    "plt.xlabel('temperature')\n",
    "plt.ylabel('rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}